The design of environment-friendly microgrids at the smart distribution level requires a stable behaviour for multiple state operations. This paper develops a Kalman filter based optimal feedback control method for the microgrid state estimation and stabilization. First, the microgrid is modelled by a discrete-time state space equation. Then the cost-effective smart sensors are deployed in order to obtain the required system information. From the communication point of view, the recursive systematic convolution code is adopted to add the redundancy in the system. At the end, the soft output Viterbi decoder is used to recover the system information from the noisy measurements and transmission uncertainties. Thereafter, the Kalman filter is utilized to estimate the system states, which acts as a precursor for applying the control algorithm. Finally, this paper proposes an optimal feedback control method to stabilize the microgrid based on semidefinite programming. The performance of the proposed approach is demonstrated by extensive numerical simulations.

en_US

dc.relation.ispartof

International Energy Journal

en_US

dc.title

Microgrid state estimation and control using Kalman filter and semidefinite programming technique

en_US

dc.type

Journal Article

utslib.description.version

Published

en_US

utslib.citation.volume

2

en_US

utslib.citation.volume

16

en_US

utslib.for

0906 Electrical And Electronic Engineering

en_US

utslib.for

0913 Mechanical Engineering

en_US

utslib.for

0906 Electrical And Electronic Engineering

en_US

utslib.for

0913 Mechanical Engineering

en_US

pubs.embargo.period

Not known

en_US

pubs.organisational-group

/University of Technology Sydney

pubs.organisational-group

/University of Technology Sydney/Faculty of Engineering and Information Technology

pubs.organisational-group

/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering

pubs.organisational-group

/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering

The design of environment-friendly microgrids at the smart distribution level requires a stable behaviour for multiple state operations. This paper develops a Kalman filter based optimal feedback control method for the microgrid state estimation and stabilization. First, the microgrid is modelled by a discrete-time state space equation. Then the cost-effective smart sensors are deployed in order to obtain the required system information. From the communication point of view, the recursive systematic convolution code is adopted to add the redundancy in the system. At the end, the soft output Viterbi decoder is used to recover the system information from the noisy measurements and transmission uncertainties. Thereafter, the Kalman filter is utilized to estimate the system states, which acts as a precursor for applying the control algorithm. Finally, this paper proposes an optimal feedback control method to stabilize the microgrid based on semidefinite programming. The performance of the proposed approach is demonstrated by extensive numerical simulations.